What Is Agentic AI? Definition, Architecture, Examples and Risks

What Agentic AI Means

Agentic AI is artificial intelligence that pursues a goal on its own by planning the steps, taking actions through software tools, observing what happened, and adjusting until the goal is met or a human needs to step in. That single sentence is the whole definition. Everything else in this guide expands on the four verbs inside it: plan, act, observe, adjust.

The expanded definition adds the machinery. An agentic AI system combines a large language model that reasons about what to do next, a planner that breaks a goal into steps, memory that carries context across those steps, tools that let the model read and write to real systems, an orchestrator that runs the loop, and guardrails that bound what the system may do without asking. Given an objective such as "work every denied claim that arrived overnight," the system decides the sequence itself, executes it, and produces both a completed task and a record of every action it took.

Why the word "agentic"

The word comes from agency, the capacity to act toward a goal rather than merely respond. In the standard AI textbook framing from Russell and Norvig, an agent is anything that perceives its environment and acts on it. A thermostat is a trivial agent. "Agentic" describes the degree of that agency: how far a system can go from a stated goal to a finished outcome without a person choosing each step. A system that only completes the next sentence has almost no agency. A system that reads a payer portal, decides an appeal is warranted, drafts it, and files it has a great deal.

One vocabulary note. An AI agent is a single software entity that runs the loop described above. Agentic AI is the broader approach, and it includes systems where several agents cooperate. The distinction is the same as the one between a worker and a way of organizing work. When people ask about "ai agents vs agentic ai" they are usually asking whether the two terms mean the same thing, and the practical answer is that every AI agent is agentic, but agentic AI also covers the multi agent systems, protocols, and governance around them.

How Agentic AI Works: The Core Loop and Components

Every agentic system, whether it manages a single inbox or a hospital revenue cycle, runs the same five step loop. The pattern was formalized in the ReAct paper by Yao and colleagues in 2022, which showed that interleaving a model's reasoning with tool calls and observations outperformed either reasoning or acting alone. Modern agent frameworks are elaborations on that idea.

• Perceive. The agent receives a trigger and gathers context: the goal, the relevant records, prior memory, and the tools available to it. In a claims workflow this is the X12 835 remittance and the patient account.
• Plan. The model decomposes the goal into steps and picks the first one. Good planners produce a plan the system can inspect and a human can read, not a hidden chain of thought.
• Act. The agent calls a tool: a FHIR query, a database write, a payer portal form, an email send. The call carries structured arguments the model generated.
• Observe. The tool returns a result, an error, or a timeout. The agent reads it and updates its working memory. This is where most real world surprises show up.
• Reflect. The agent compares the observed state with the goal, decides whether to continue, re plan, or stop, and checks whether the next action crosses a line that requires human approval.

The components of an agentic AI architecture

The loop is simple. The components that make it dependable are not. The table below lists each one, what it does, how teams typically implement it, and what breaks when it is missing.

A useful rule from our own delivery work: the model accounts for a small share of the engineering hours in a production agent. Tool integrations, state management, evaluation, and the approval workflow account for the rest. That is why AI agent development looks far more like systems integration than like prompt writing once a project leaves the demo stage.

The Five Levels of Agentic AI

Autonomy is not a switch. It is a dial, and buyers need a shared scale to talk about where a given system sits. The five level framework below is our own, modeled loosely on the way SAE J3016 grades driving automation from 0 to 5. It is not a vendor standard, and you will see other schemes with different boundaries. What matters is that each level answers the same question: who decides the next action, and who confirms it?

In regulated industries nearly every deployment we see in production sits at level 3 or level 4. Level 3 is where trust is earned: the agent does the reasoning and the legwork, and a person confirms the action that changes data or money. Level 4 is reached one action type at a time, after the approval logs show the agent's proposals are being accepted without edits. Level 5 belongs to low stakes, reversible work such as internal research, not to anything that touches a patient record or a general ledger.

Single Agent vs Multi Agent Systems

A single agent system is one loop, one model context, one set of tools, and one goal. It is the right default. A multi agent system splits the work across several agents, each with its own instructions, tools, and memory, coordinated by some structure. Teams move to multiple agents when a single context window cannot hold the whole task, when different steps need different tools or permissions, or when they want one agent to check another's work.

Four common multi agent patterns

• Orchestrator and workers. A lead agent decomposes the goal, hands sub tasks to specialist agents, and assembles the results. Fits work with clear sub domains, such as a close process where one worker reconciles bank feeds and another handles intercompany entries.
• Sequential pipeline. Agents pass output down a fixed chain: extract, validate, enrich, file. Each stage has one job and one set of permissions. Fits high volume document workflows like intake or invoice processing where the order never changes.
• Debate or critic. One agent produces, a second agent critiques against a rubric, and the first revises. This is the same idea behind LLM as a judge evaluation, applied at run time instead of in testing. Fits drafting tasks where quality matters more than speed, such as payer appeals.
• Hierarchical. Layers of orchestrators manage layers of workers, with escalation up the tree. Fits large operations where a top level agent owns an outcome and delegates regions or departments. Also the hardest to debug.

The cost of going multi agent is real. Every additional agent adds tokens, latency, and a new place for an error to originate or hide. A workable rule is to start with one agent and split only when you can name the specific limit you hit. Agentic workflows built around a single supervised agent with good tools outperform sprawling agent teams on most enterprise tasks we scope.

Protocols and Standards for Agentic AI

Early agents were wired to each tool by hand, so every model and every system needed its own adapter. Three developments between 2023 and 2025 turned that into a standards problem with standards answers. Each is described here as it exists, without vendor claims about which will win.

• Function calling (OpenAI, June 2023). OpenAI added the ability for a model to return a structured JSON call to a developer defined function instead of free text. The developer describes each function with a JSON Schema, the model picks one and fills the arguments, and the application executes it. Every major model provider now offers an equivalent, and it remains the primitive underneath most agent frameworks.
• Model Context Protocol (Anthropic, November 2024). MCP is an open protocol that standardizes how an AI application connects to external data sources and tools. A tool provider ships one MCP server, and any MCP compatible client can discover and call its tools, resources, and prompts. Anthropic released it as open source in late 2024, and OpenAI and Google both announced support during 2025, which made it the de facto tool connection layer.
• Agent2Agent protocol (Google, April 2025). A2A addresses a different layer: how independent agents from different vendors discover each other, exchange tasks, and report status. Google announced it at Cloud Next in April 2025 with a group of launch partners and later contributed the project to the Linux Foundation. Where MCP connects an agent to tools, A2A connects an agent to other agents.

For a buyer the practical implication is portability. Ask any vendor whether their tools are exposed through MCP or an equivalent open interface, and whether their agents can be swapped to a different model without rewriting integrations. Agent frameworks change quickly. A tool layer built to a standard survives those changes; one built to a single vendor's SDK does not.

Agentic AI vs Generative AI, RPA, and Traditional Automation

Agentic AI vs generative AI

Generative AI produces content in response to a prompt and stops. Agentic AI uses a generative model as its reasoning engine but adds planning, tools, memory, and controls so that it can act. They are layers, not rivals. We cover the decision framework, overlap, and enterprise use cases in depth in Agentic AI vs Generative AI, so this guide does not repeat it.

Agentic AI vs RPA vs rules based automation

The more useful comparison for operations leaders is with the automation they already own. Robotic process automation drives existing user interfaces with scripted bots. Traditional automation covers workflow engines, scheduled jobs, and integration platforms that follow explicit rules. Agentic AI differs on one axis above all others: it decides what to do when the input does not match a rule that someone wrote in advance.

These coexist in practice. An agent frequently calls an RPA bot as a tool to reach a payer portal that has no API, and hands a fully specified sub task to a workflow engine because determinism is cheaper than reasoning. The agent adds value where a human used to sit: at the decision points between the automated pieces.

Agentic AI Examples by Industry

The examples below describe what each agent does and where it sits on the five level scale. They are drawn from the agent catalog Bonami ships under Bonami X AI. For a wider catalog across more industries, see our list of AI agent examples running in production.

Healthcare

Healthcare administration is the strongest fit for agentic AI use cases we have found, because the work is high volume, rules heavy, spread across payer and provider systems, and measured in deadlines. It is also the most demanding environment for controls, since nearly every record is protected health information. Our healthcare AI agents page covers the full set; five representative agents follow.

• Prior authorization. The prior authorization agent reads the order, pulls the payer's medical policy, assembles the clinical documentation into a packet, submits through the payer channel, and tracks status until a decision arrives. The CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F, January 2024) requires impacted payers to decide urgent requests within 72 hours and standard requests within 7 calendar days starting in 2026, which gives this agent a clock to work against. Level 3: a nurse or coordinator approves the packet before submission.
• Denial management. The denial management agent ingests each X12 835 remittance, classifies the denial by CARC and RARC code and root cause, retrieves the supporting record, drafts a payer specific appeal, and queues it for a biller. Level 3, moving toward level 4 for low dollar administrative denials with a proven appeal template.
• Eligibility verification. Ahead of each scheduled visit the agent runs X12 270/271 eligibility checks, compares the response to the coverage on file, flags terminated plans and missing authorizations, and writes verified coverage back to the practice management system. Level 4 for clean matches, level 3 for discrepancies.
• Patient intake. The agent sends the pre visit forms, converses with the patient to complete history and consent, reconciles the answers with the existing chart, and stages the encounter in the EHR for clinical review. Level 3: nothing enters the legal record until a clinician accepts it.
• Post discharge follow up. After discharge the agent contacts the patient on the schedule in the care plan, asks structured symptom and medication questions, scores the responses against escalation criteria, and alerts the care team when a threshold is crossed. Level 2 to 3: the agent gathers and routes, and clinicians make every clinical decision.

Enterprise operations

• Finance close and invoice exceptions. The invoice exception handler picks up every invoice that fails a three way match, investigates the purchase order, receipt, and contract, determines whether the variance is a price, quantity, or timing issue, and either resolves it inside policy or routes it with a recommendation. During close the same pattern chases open items, drafts accrual proposals, and explains variances for a controller to accept.
• IT incident triage. The incident triage agent reads an alert, correlates it with recent deploys, change tickets, and similar past incidents, assigns a severity, pages the right on call engineer, and drafts the first status update. Read actions run at level 4; remediation actions such as a rollback stay at level 3 with an engineer approving.
• Procurement. A procurement agent intakes a purchase request, checks it against budget and preferred vendor policy, gathers quotes, prepares the comparison, and routes the approval chain. It also monitors contract renewal dates and opens the renegotiation workflow ahead of auto renewal. Level 3 for anything that commits spend.

The pattern across all of them: the agent absorbs the reading, looking up, drafting, and routing. A person keeps the decision that changes money, a record, or a patient's care. That division is what makes agentic AI acceptable to compliance teams, and it is the reason the first agent in an organization should be chosen for volume, not for glamour.

Related Guides

These guides cover the neighboring concepts this article refers to, each written in the same answer first format.

• What Is Conversational AI? How It Works, Types and Enterprise Use Cases: how conversational AI works and how it differs from chatbots and agents.
• What Is Prior Authorization? Process, Timelines and the 2026 Rules: the prior authorization process, turnaround rules and electronic PA standards.
• What Is Revenue Cycle Management? The 2026 Guide to Healthcare RCM: the full revenue cycle, its 12 steps and the KPIs that measure it.
• HIPAA Privacy Rule vs Security Rule: What Each Covers, Who Complies: what the Privacy Rule and Security Rule each govern and who must comply.

Agentic AI Risks, Failure Modes, and Controls

A generative model that is wrong produces a bad draft. An agent that is wrong produces a bad action, and then bases its next action on it. Autonomy multiplies the consequences of every model error, which is why the risk discussion for agentic AI is about the surrounding system, not the model's benchmark score. The six failure modes below account for nearly every incident we have seen or read about.

The six controls every production agent needs

• Permissions scoping. The agent runs under its own service identity with the minimum access its task requires. Read and write are separate scopes. A denial agent can read the clinical record; it cannot edit it.
• Approval gates. Actions are classified in advance as auto, confirm, or forbidden. Confirm actions pause the loop and wait for a person. The classification is a policy artifact that compliance can review, not a prompt.
• Sandboxing. Code execution, browsing, and file handling happen in isolated environments with no route to production credentials. Anything the agent fetches from outside is quarantined as data.
• Audit logs. Every step records the input, the plan, the tool call, the arguments, the result, and the model's stated reason. Under HIPAA this also satisfies the audit controls standard at 45 CFR 164.312(b).
• Evaluation. Before any promotion in autonomy, the agent runs against a labeled test set and its live approval logs are scored. Rubric based grading with a second model, described in our guide to LLM as a judge evaluation, scales this beyond what humans can review by hand.
• Kill switches. One control stops every run for a given agent, revokes its credentials, and holds queued actions. It is tested on a schedule, the same way a disaster recovery plan is.

None of these is exotic. They are the same controls a security team applies to a new employee or a new integration, adapted to a system that reasons in natural language. The difference is discipline: they must exist before the agent gets write access, not after the first incident.

Regulation Touchpoints: EU AI Act, NIST AI RMF, HIPAA

No regulation today is written specifically for agentic AI, but three frameworks already govern how agents can be built and deployed. Legal review belongs with counsel; the summary below tells a buyer which questions to bring to that review.

EU AI Act. Regulation (EU) 2024/1689 entered into force on August 1, 2024. It sorts AI systems into four risk tiers: unacceptable, high, limited, and minimal. Prohibited practices took effect February 2, 2025, obligations for general purpose AI models on August 2, 2025, and most high risk obligations on August 2, 2026 under the original schedule. Agents used in employment decisions, credit, essential services, or as safety components fall into the high risk tier and require risk management, logging, human oversight, and conformity assessment. Penalties for prohibited practices reach 35 million euros or 7 percent of global turnover, whichever is higher.

NIST AI RMF. The NIST AI Risk Management Framework 1.0, published in January 2023, is voluntary but has become the reference vocabulary for US enterprise AI governance. Its four functions, Govern, Map, Measure, and Manage, map cleanly onto the agent controls above. NIST added a Generative AI Profile (NIST AI 600-1) in July 2024 that addresses risks such as confabulation and information security relevant to any LLM driven agent. Procurement teams increasingly ask vendors to show alignment with it.

HIPAA. When an agent touches protected health information, the vendor operating it is a business associate and needs a signed business associate agreement under 45 CFR 164.502(e). The Security Rule technical safeguards at 45 CFR 164.312 require access control, audit controls, integrity protection, and transmission security, and the minimum necessary standard at 164.502(b) limits what PHI reaches the model at all. ISO/IEC 42001, published in December 2023, provides a certifiable AI management system standard that many health systems now ask about alongside SOC 2. For a deeper treatment of program design, see our guide to enterprise AI governance, risk, and compliance.

How to Start With Agentic AI

The organizations that get value from agentic AI in the first year share one habit. They do not begin with a general purpose assistant. They pick one workflow, instrument it, and let the agent earn autonomy on that workflow before touching the next. The sequence below is the one we follow.

• Pick a narrow, high volume workflow. Look for work that happens hundreds of times a week, follows rules a senior operator could write down, spans two or more systems, and is currently limited by staff time. Denials, eligibility, invoice exceptions, and tier one incident triage all qualify.
• Define success metrics before building. Choose two or three: touch time per item, first pass acceptance rate of the agent's proposals, cycle time, and cost per item. Baseline them on the manual process for at least a month.
• Map the tools and permissions. List every system the agent must read and every one it must write. Get the API or MCP access sorted before model work begins. This step takes longer than anyone expects.
• Keep a human checkpoint. Launch at level 3. The agent does everything except the final write, and a person approves from a queue. Their edits are the training signal and the audit trail.
• Promote autonomy by evidence. When the approval logs show a category of action is accepted unchanged at a rate the business owner is comfortable with, move that one category to level 4. Leave the rest at level 3.

Agentic AI buyer checklist

Whether you build in house or work with a partner, these are the questions that separate a production ready proposal from a demo.

• Which autonomy level does the agent launch at, and what evidence promotes an action type to the next level?
• What is the complete list of tools the agent can call, and what credentials does each one use?
• Which actions are auto, confirm, and forbidden, and where is that policy stored so compliance can review it?
• How is tool output isolated from instructions to defend against prompt injection?
• What does the audit log capture per step, how long is it retained, and who can query it?
• What are the per run limits on steps, tokens, and spend, and what happens when one is hit?
• How is the agent evaluated before release and after each model or prompt change?
• Can the model be swapped without rewriting the tools? Are the tools exposed through MCP or an equivalent open interface?
• For PHI: is there a business associate agreement, and where does data reside at rest, in transit, and in logs?
• What is the kill switch, who can pull it, and when was it last tested?

If you want help choosing the first workflow and setting the autonomy policy before committing to a build, that is the scope of our AI consulting engagements. If you already know the workflow, the agent development team takes it from there.